Evaluation of Communication and Human Response Latency for (Human) Teleoperation
Bibliographic record
Abstract
We previously introduced a novel mixed reality teleguidance system dubbed human teleoperation (David Black et al., 2023 and Black and Salcudean, 2023), in which a human (expert) leader and a human (novice) follower are tightly coupled through mixed reality and haptics. Our first evaluation of human teleoperation is in the context of tele ultrasound, in which a sonographer or radiologist’s gestures are copied by a remote novice to carry out an ultrasound examination. In this paper, a communication system suitable for implementation of human teleoperation is presented and characterized in various network conditions, over Ethernet, Wi-Fi, 4G LTE, and 5G. To obtain a full understanding of latency in the system, the human response time is additionally characterized through a series of step response tests with 11 volunteers. The step responses were obtained by tracking the position of, and force exerted by, the human hand in response to a change in the mixed reality target. Different rendering methods were evaluated. The round-trip communication latency is 40 ± 10 ms over 5G, and down to 1 ± 0.6 ms over Ethernet for typical throughputs. The human response time to a step change in position depends on the step magnitude, but is between 485 to 535 ms, while the reaction time to a change in force is 150 to 200 ms. Both lag times are greatly decreased when tracking a smooth motion. Thus, we demonstrate that the system is network agnostic and can achieve good teleoperation performance and secure, low latency communication in appropriate network conditions. This brings the human teleoperation concept a step closer to human trials in a clinical environment, and the presented tools and concepts are applicable to any high-performance teleoperation system, and especially for mixed reality guidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".